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Step Length, but Not Stepping Cadence, Strongly Predicts Physical Activity Intensity during Jogging and Running
- Source :
-
Measurement in Physical Education and Exercise Science . 2023 27(4):352-361. - Publication Year :
- 2023
-
Abstract
- Device-based measures often rely on the positive relationship between walking cadence and metabolic equivalents of task (METs) to estimate physical activity. It is unknown whether this relationship remains during jogging/running. The study purpose was to investigate the relationships between METs, cadence, and step length during walking and jogging/running. A treadmill protocol with 5 walking (3.2-6.4 km•hr[superscript -1]) and 5 jogging/running stages (8.0-11.3 km•hr[superscript -1]) was completed in 43 adults (23 ± 5 years, 19[female gender symbol]). Predictors of METs during walking and jogging/running were determined by generalized mixed modeling. The strongest prediction models for walking (R[superscript 2] = 0.72, P < 0.001) and jogging/running (R[superscript 2] = 0.75, P < 0.001) included cadence[superscript 2], cadence, step length, age, and leg length (all, P < 0.001). Step length accounted for 49.1% and 78.3% of model variance during walking and jogging/running, respectively. METs are poorly estimated by cadence during jogging/running but step length reduces error. Strategies to measure step length in free-living settings could better predict physical activity intensity.
Details
- Language :
- English
- ISSN :
- 1091-367X and 1532-7841
- Volume :
- 27
- Issue :
- 4
- Database :
- ERIC
- Journal :
- Measurement in Physical Education and Exercise Science
- Publication Type :
- Academic Journal
- Accession number :
- EJ1398886
- Document Type :
- Journal Articles<br />Reports - Research
- Full Text :
- https://doi.org/10.1080/1091367X.2023.2188118